<![CDATA[The Extention of MFCC Technique from 1D to 2D as Feature Extractor for Speaker Identification System Using HMM. In this paper, we introduce an extension of Mel-Frequency Cepstrum Coefficients (1D-MFCC) methodology to bispectrum data, referred to as 2D-MFCC, for feature extraction. 2D-MFCC is based on 2D bispectrum data rather than 1D spectrum vector yielded by Fourier transform, so the filter in 1D-MFCC must be extend to 2D filter and using 2D cosine transform to get the mel-cepstrum coefficients from the filtered bispectrum values. Based on 2D-MFCC, we develop a speaker recognition system with Hidden Markov Model (HMM) as classifier. The experimental results show that the recognition rate is around 88%, 92% and 99% for 20, 40 and 60 data training, respectively]]>
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